DE 199 59 019 A1 discloses a method for detecting a state of an energy accumulator by means of which precise and reliable energy accumulator diagnostics can be carried out using a model, a filter and a parameter estimator. By means of the estimation of parameters it is possible to determine model parameters which come about owing to the aging of the energy accumulator or owing to defects, and to continuously approximate and adjust the model, on which the estimations are essentially based, to the actual state.
For the purpose of monitoring safety and to ensure a service life which is as long as possible, it is also known to measure the voltage of each individual cell together with the battery current and the battery temperature and to perform a state estimation in respect of the state of charge and/or the state of health.
In technically high-quality battery management systems control observer structures are used. Such an observer structure is illustrated, for example, in FIG. 1.
An observer is understood to be a system which determines and/or derives states by means of a model and by using known, defined input variables and/or measurement variables. The magnitude of such states usually cannot be measured owing to their complexity, or can be measured only at very high cost.
In the model, the observer therefore models an actual controlled system or a real system. It can comprise a controller which models the measurable state variables. A known observer is what is referred to as the Luenberger observer.
By using an observer or an observer structure as illustrated in FIG. 1, it is possible to precisely determine the states of the service life and the performance of a battery pack at any time. The cell models which are used here correspond to theoretical figures or mathematical models. They have numerous parameters for describing the capacity and the impedance of the individual cell. Energy contents and performance of the cells and of the entire pack as well as service life predictions are calculated from these parameters. These values can also be parameters of the cell models and/or of the observer structure.
The parameters themselves very frequently have multi-dimensional dependencies on state variables such as, for example, temperature, state of charge, current strength and similar variables. This therefore results in complex parameter spaces in which the parameters can be observed. A dedicated observer is assigned to each cell.
For the purpose of correcting each cell model, adjustment has to be continuously made between the cell model and the respective cell with respect to the conditions or their variables, such as for example temperature, which are actually prevailing at or in the respective battery cell.
Conventional observer structure 20 is, as illustrated in FIG. 1, arranged parallel to a battery cell 10. The value of a current IBat which flows through the battery cell 10 is fed as information to the observer structure 20. Likewise, the value of a temperature, measured at or in the battery cell 10, of the cell Tcell is fed to the observer structure 20. The voltage Vcell which is measured at the battery cell 10 is compared with the model voltage VMod determined in the cell model of the observer structure 20, and the value of the resulting difference voltage 50 is fed again to the observer structure 20. The states of charge 30 and of health 40 are estimated by the observer structure 20.
The illustrated observer structure 20 is not restricted to determining the state of charge 30 and the state of health 40 alone here but rather further states can also be determined depending on requirements and as a function of the calculation capacity. Likewise, the observer structure 20 is not restricted to the inputting of the values of the current strength, temperature and voltage.
When the observer structure is applied for sensing states of battery cells, each individual cell is adjusted not only in its state of charge but also in age-specific parameters such as what is referred to as the “state of health” (SOH), using the cell model and the control observer structure.
It is disadvantageous here that this process entails enormous computational complexity and memory requirements, which also increase proportionately with the number of cells. This is unfavorable with respect to the scalability, usually desired by the automobile industry, of battery platform systems which are essential in performance which differs with respect to the reduction of complexity and costs in the manufacture of battery systems of differing performance, and the computing power which is required as a result.